Calendar

During the fall and spring semester the Computational Social Science (CSS) and the Computational Sciences and Informatics (CSI) Programs holds weekly seminars where students, faculty and guest speakers present their latest research. These seminars are free and are open to the public.

For CSI, the seminars take place in Exploratory Hall, Room 3301 on Mondays from 4:30 p.m. to 5.40 pm.

For CSS, the seminars take place in Center for Social Complexity Suite which is located Research Hall, Level 3. The seminars start at 3:00 p.m. and normally last until 4:30 p.m. For a list of past CSS seminars click here.

In addition we also host ad hoc seminars relating to guest speakers and students dissertation proposals/defenses which don’t fall under our normal seminars.

If you would like to join the seminar mailing list please email Karen Underwood.

The CSS seminar format for Friday, April 21 will be an “Open Mic” session for CSS PhD students to present their research ideas to their peers prior to starting their projects. Peer feedback in encouraged at this event

Some points for presenters to consider are:

Do you have a paper that is ‘stalled’ and in need of some help to push it to the finish line?

Is one of your models producing interesting results but also doing wacky things?

Do you have exciting results but can’t figure out how to visualize/display them?

Do you need advice on how to calibrate/estimate your theoretical model with data?

The ‘Hidden Trump Model’: Modeling social desirability bias through ABMs

Social desirability bias is a tendency people have to lie about their opinions if they perceive they will be judged or rejected. We present an Opinion Dynamics model in which agents may not be truthful about their opinions when they interact with their social circle. We model two processes through which agents influence one another: an online anonymous process in which agents can interact with anyone and do not fear social rejection, and a face-to-face process where they interact only with friends and may feel compelled to conform. In a political setting, this would apply to a race in which one of the candidates bears a social stigma and therefore some agents are reluctant to voice support for him or her. The results that these nonlinear and asymmetrical processes will have on the overall electorate are not obvious, and are well-suited to an agent-based study.

We hypothesize that this model will produce a “poll bias” of the kind we saw in the 2016 Presidential election — i.e., a significant difference between the number of agents who say they will vote for a candidate and the number who actually do so on election day. We present an analysis of this “Hidden Trump model” and describe the way in which poll bias depends on the strength of the various interaction processes.

Abstract: In January of 2017, Forbes Magazine listed the top technical job skills showing the highest growth in demand from 2011 to 2015. The number three position, with 1,581% growth, was Tableau, a software solution that helps people see and understand their data.

Tableau offers free licenses for academic research.

In this session, Paul Albert will:

Provide a hands-on overview of Tableau to show how it can help people do more with their data

Show examples of Tableau data visualizations relevant to the CSS world

Discuss how Tableau might be able to alter paradigms for sharing academic findings

Discuss free resources available to learn more about Tableau

Paul recently retired from Tableau and has started graduate studies with the GMU Art History program. His initial focus is on applying quantitative analysis and social theory to art markets. His secondary focus is on exploring how products like Tableau can be used to support new ways of presenting academic research and findings.

While at Tableau, Paul coordinated and conducted over 60 training events for over 1,100 participants. Paul was also one of four finalists, out of a field of over 200 contestants, in the annual Tableau “Viz Wiz” data visualization contest for 2016.

The Mercatus Center at George Mason University recently launched QuantGov, an open-source policy analytics platform designed to facilitate policy-relevant research. QuantGov deploys text analysis and machine-learning algorithms to identify the latent governance indicators buried in policy documents, such as legislation or administrative code. QuantGov grew out of the RegData project, which was designed to capture novel metrics in regulations that would advance our understanding of the United States’ federal regulatory process in ways that were previously infeasible. QuantGov is the next generation in that project’s evolution.

ABSTRACT: This talk will be a review of calibration methods for classifiers that make probabilistic predictions on a scale 0 to 1. It is known that certain classification methods, such as Naïve Bayes or Random Forest make biased predictions that to not match the true posterior probabilities. By calibrating the predictions made by classifiers the true probability of the predicted class can be determined. This type of calibration can be crucial for real-world decision making problems in medicine, business, marketing, and finance. In this talk I will focus on applications in marketing.

Part two: Marketing yourself for future careers outside of academia

It is known that the number of jobs in academia is not rising as fast as the number of PhD’s graduating. Currently a new career option is available to these PhDs, the Data Scientist. But how does one make the transition out of academia to this hot new field? I will discuss strategies for marketing yourself as well as tools necessary to be successful in your transition.

Dr. Oscar Olmedo is an alumnus of George Mason University who studied physics in undergrad (2004), Computational Sciences and Informatics Masters (2007), and Computational Sciences and Informatics PhD (2011) with a concentration in solar physics under Dr. Jie Zhang. After graduating in 2011, Dr. Olmedo went on to NRL as an NRC fellow for two years, and briefly worked at NASA Goddard for a few months in 2013 before moving to Syntasa, a startup focusing on ecommerce/marketing analytics. In 2015, he moved to CACI to work on cyber security research as a DARPA contractor.

ABSTRACT: This talk will review the status of our multi-year project to characterize the reaction of the population of a US megacity to a nuclear WMD event. Our approach is to develop an agent-based model of the New York City area, with agents representing each of the 20-25 million people, and establish a baseline of normal behaviors before exploring the population’s reactions to small (5-10Kt) nuclear weapon explosions. In our first year, we explored understanding a large population’s reaction to a nuclear WMD event with four major activities: (1) reviewing existing social theories and reports of disaster behavior, (2) collecting data and modeling the infrastructure of a mega-city and surrounding region, (3) generating synthetic population, and (4) developing an agent-based model of all the individuals in the region. The review of social science theories and data on individual/group behavior during disasters led to the publication of a case study (the Flint River drinking water crisis) and preparation of two review papers. For the New York City mega-city and surrounding area, we collected spatial, demographic, and workforce data from several sources and devised methods and algorithms to make the data useful for our simulation. Using Python, we processed road data and created one connected network forming the transportation layer of the model. Using demographic data and our own heuristics, again in Python, we synthesized individuals, their households, their associated schools and workplaces and finally their social networks. Other datasets were utilized so that children attend nearby schools or daycare constrained with actual capacities and people are employed in workplaces located nearby matching workforce data. Finally, we began modeling individuals’ movement in three counties, two rural counties and one in the heart of Manhattan. I will start with a discussion of the effects of a nuclear WMD event and then discuss the details our work and our future plans.

Claudio Cioffi-Revilla, Professor
Computational Social Science
Department of Computational and Data Sciences
Director, Center for Social Complexity
George Mason University

Computational Modeling of Terrorism

Monday, September 11, 4:30-5:45
Exploratory Hall, Room 3301

Computational social scientists have investigated terrorism for decades, but only recently has the field advanced to creating the first testable formal theories. This talk will review some important background and present recent advances in agent-based modeling of terrorism, based on radicalization theory and research. Enduring challenges will also be covered, as opportunities for research projects, theses, and dissertations.

Dr. Cioffi-Revilla is a Professor of Computational Social Science, founding and former Chair of the Department of Computational Social Science, and founding and current Director of the Mason Center for Social Complexity at George Mason University. He holds two doctoral degrees in Political Science and International Relations and his areas of special interest include quantitative, mathematical, and simulation models applied to complex human and social systems.

The behavior of financial markets has, and continues, to frustrate investors and academics. With the advent of new approaches, including complex systems and network analysis, the search for an explanation as to how and why markets behavior as they do has greatly expanded, and moved away from the tradition neoclassical approaches that have been beholden to the Efficient Market Hypothesis.

The complex system approach utilizes a number of a concepts in an attempt to understand stock market returns including; imitation, herding, self-organized co-operativity, and positive feedbacks, with many of these features captured by network analysis. In addition, with the meteoric rises of network science has come the realization that the behavior of a system can vary greatly depending on the network structure (the topology) of a system, thus providing further impetus for the use of network analysis in terms of financial markets.

My presentation will detail my recent research of the US Institutional shareholder networks over the period of 2007-10, a period which includes the beginning of the Global Financial Crisis. The research utilized an extensive dataset provided from the Thomson Reuters 13f database, to undertake a temporal analysis of the networks formed between US institutional investors and the stocks in the S&P 500. The analysis makes use of both projected and bipartite networks and uncovers numerous insights regarding relationships between the market in general, stocks and their shareholders. In addition, I will illustrate how the findings can be used in conjunction with an agent-based model to uncover the workings of the stock market.

Dr. Simpson is a seasoned researcher in the areas of machine learning, deep learning, and cybersecurity. He has served as Principal Investigator and Data Scientist on several DARPA programs with a focus on machine learning applications to data fusion, prediction, and unsupervised anomaly detection problems. He holds a Ph.D. in Electrical Engineering from North Carolina State University where he developed novel receivers and algorithms for undersea communications.

While great advances in modeling have been made, one of the greatest challenges we face is that of understanding human behavior and how people perceive and behave in physical spaces. Can new sources of data (i.e. “big data”) be used to explore the connections between people and places?

In this presentation, I will review the current state of art of modeling geographical systems. I will highlight the challenges and opportunities through a series of examples that new data can be used to better understand and simulate how individuals behave within geographical systems.